130,063 research outputs found
Early warning signals in plant disease outbreaks
Infectious disease outbreaks in plants threaten ecosystems, agricultural crops and food trade. Currently, several fungal diseases are affecting forests worldwide, posing a major risk to tree species, habitats and consequently ecosystem decay. Prediction and control of disease spread are difficult, mainly due to the complexity of the interaction between individual components involved. In this work, we introduce a lattice-based epidemic model coupled with a stochastic process that mimics, in a very simplified way, the interaction between the hosts and pathogen. We studied the disease spread by measuring the propagation velocity of the pathogen on the susceptible hosts. Our quantitative results indicate the occurrence of a critical transition between two stable phases: local confinement and an extended epiphytotic outbreak that depends on the density of the susceptible individuals. Quantitative predictions of epiphytotics are performed using the framework early-warning indicators for impending regime shifts, widely applied on dynamical systems. These signals forecast successfully the outcome of the critical shift between the two stable phases before the system enters the epiphytotic regime. Our study demonstrates that early-warning indicators could be useful for the prediction of forest disease epidemics through mathematical and computational models suited to more specific pathogenâhost-environmental interactions. Our results may also be useful to identify a suitable planting density to slow down disease spread and in the future, design highly resilient forests
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Update of an early warning fault detection method using artificial intelligence techniques
This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Todayâs presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a âcrystal ballâ view of future developments in the operation and monitoring of transmission systems in the next millennium
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Off-balance sheet exposures and banking crises in OECD countries
Against the background of the acknowledged importance of off-balance-sheet exposures in the sub prime crisis, we seek to investigate whether this was a new phenomenon or common to earlier crises. Using a logit approach to predicting banking crises in 14 OECD countries we find a significant impact of a proxy for the ratio of banksâ off-balance-sheet activity to total (off and on balance sheet) activity, as well as capital and liquidity ratios, the current account balance and GDP growth. These results are robust to the exclusion of the most crisis prone countries in our model. For early warning purposes we show that real house price growth is a good proxy for off balance sheet activity prior to the sub-prime episode. Variables capturing off-balance sheet activity have been neglected in most early warning models to date. We consider it essential that regulators take into account the results for crisis prediction in regulating banks and their off-balance sheet exposures, and thus controlling their contribution to systemic risk
Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks
Vehicular adhoc networks allow vehicles to share their information for safety
and traffic efficiency. However, sharing information may threaten the driver
privacy because it includes spatiotemporal information and is broadcast
publicly and periodically. In this paper, we propose a context-adaptive
pseudonym changing scheme which lets a vehicle decide autonomously when to
change its pseudonym and how long it should remain silent to ensure
unlinkability. This scheme adapts dynamically based on the density of the
surrounding traffic and the user privacy preferences. We employ a multi-target
tracking algorithm to measure privacy in terms of traceability in realistic
vehicle traces. We use Monte Carlo analysis to estimate the quality of service
(QoS) of a forward collision warning application when vehicles apply this
scheme. According to the experimental results, the proposed scheme provides a
better compromise between traceability and QoS than a random silent period
scheme.Comment: Extended version of a previous paper "K. Emara, W. Woerndl, and J.
Schlichter, "Poster: Context-Adaptive User-Centric Privacy Scheme for VANET,"
in Proceedings of the 11th EAI International Conference on Security and
Privacy in Communication Networks, SecureComm'15. Dallas, TX, USA: Springer,
June 2015.
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Early warning fault detection using artificial intelligent methods
This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper
Cross-Layer Design to Maintain Earthquake Sensor Network Connectivity After Loss of Infrastructure
We present the design of a cross-layer protocol to maintain connectivity in
an earthquake monitoring and early warning sensor network in the absence of
communications infrastructure. Such systems, by design, warn of events that
severely damage or destroy communications infrastructure. However, the data
they provide is of critical importance to emergency and rescue decision making
in the immediate aftermath of such events, as is continued early warning of
aftershocks, tsunamis, or other subsequent dangers. Utilizing a beyond
line-of-sight (BLOS) HF physical layer, we propose an adaptable cross-layer
network design that meets these specialized requirements. We are able to
provide ultra high connectivity (UHC) early warning on strict time deadlines
under worst-case channel conditions along with providing sufficient capacity
for continued seismic data collection from a 1000 sensor network.Comment: To be published in MILCOM 2012 - Track 2: Networking Protocols and
Performanc
Using Shock Index as a Predictor of ICU Readmission: A Quality Iimprovement Project
Background: Adverse events will occur in one-third of patients discharged from the intensivecare unit (ICU) and evidence shows that ICU readmissions increase a patientâs length of stay,mortality, hospital costs, and nosocomial infections, as well as decrease long-term survival.Specific predictive factors that will accurately predict which patients are at risk of adverseevents requiring readmission are needed.Aim: The specific aim of this project was to identify if shock index (SI) values higher than 0.7at the time of transfer from the ICU are a useful predictor of ICU readmission.Methods: Using the Plan, Do, Study, Act (PDSA) framework, a retrospective chart review wasperformed using a matched cohort of 34 patients readmitted with 72 hours of discharge from theICU and 34 controls to obtain SI values at admission, transfer from and readmission to the ICU.A second PDSA cycle looked for SI trends within 24 hours prior to discharge from the ICU.Results: An odds ratio calculating the risk of readmission of patients with an elevated SI was2.96 (Confidence Interval (CI) 1.1 to 7.94, p-value=0.03). The odds ratio for an 80% SIelevation over 24 hours prior to discharge was 1.56 (CI 0.36 to 6.76, p-value=0.55).Conclusion and Implications for CNL Practice: Patients with elevated SIs at the time oftransfer are three times more likely to be readmitted to the ICU. Patients with elevations in atleast 80% of the 24 hour pre-discharge SIs showed no significant differences between thecontrol and readmitted cohorts. Implications of these results for the clinical nurse leader will bediscussed
Catastrophic Phase Transitions and Early Warnings in a Spatial Ecological Model
Gradual changes in exploitation, nutrient loading, etc. produce shifts
between alternative stable states (ASS) in ecosystems which, quite often, are
not smooth but abrupt or catastrophic. Early warnings of such catastrophic
regime shifts are fundamental for designing management protocols for
ecosystems. Here we study the spatial version of a popular ecological model,
involving a logistically growing single species subject to exploitation, which
is known to exhibit ASS. Spatial heterogeneity is introduced by a carrying
capacity parameter varying from cell to cell in a regular lattice. Transport of
biomass among cells is included in the form of diffusion. We investigate
whether different quantities from statistical mechanics -like the variance, the
two-point correlation function and the patchiness- may serve as early warnings
of catastrophic phase transitions between the ASS. In particular, we find that
the patch-size distribution follows a power law when the system is close to the
catastrophic transition. We also provide links between spatial and temporal
indicators and analyze how the interplay between diffusion and spatial
heterogeneity may affect the earliness of each of the observables. We find that
possible remedial procedures, which can be followed after these early signals,
are more effective as the diffusion becomes lower. Finally, we comment on
similarities and differences between these catastrophic shifts and paradigmatic
thermodynamic phase transitions like the liquid-vapour change of state for a
fluid like water
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